Tracking Perceptual and Memory Decisions by Decoding Brain Activity

Decision making is thought to involve a process of evidence accumulation, modelled as a drifting diffusion process. This modeling framework suggests that all single-stage decisions involve a similar evidence accumulation process. In this paper we use decoding by machine learning classifiers on intracranially recorded EEG (iEEG) to examine whether different kinds of decisions (perceptual vs. memory) exhibit dynamics consistent with such drift diffusion models. We observed that decisions are indeed decodable from brain activity for both perceptual and memory decisions, and that the time courses for these types of decisions appear to be quite similar. Moreover, the high spatial resolution of iEEG reveals that perceptual and memory decisions rely on slightly different brain areas. While the accuracy of decision decoding can stil be improved, these initial studies demonstrate the power of decoding analyses to examine computational models of cognition